A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface

2021 ◽  
Author(s):  
Francesco Mattioli ◽  
Camillo Porcaro ◽  
Gianluca Baldassarre
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Data Augmentation ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Single Individual ◽  
Related Data ◽  
Motor Movement ◽  
Average Accuracy ◽  
Eeg Recordings

Abstract Objective: Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing. Approach: We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a ‘baseline’ class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its outer layers with only 12-minute individual-related data. Main results: The model tested with the ‘EEG Motor Movement/Imagery Dataset’ outperforms the current state-of-the-art models by achieving a 99.38% accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of 99.46%. Significance: The proposed methods could foster future BCI applications relying on few-channel portable recording devices and individual-based training.

scholarly journals Tangent Space Features-Based Transfer Learning Classification Model for Two-Class Motor Imagery Brain–Computer Interface

2019 ◽  
Vol 29 (10) ◽  
pp. 1950025 ◽  
Author(s):  
Pramod Gaur ◽  
Karl McCreadie ◽  
Ram Bilas Pachori ◽  
Hui Wang ◽  
Girijesh Prasad
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Training Session ◽  
Training Data ◽  
Classification Model ◽  
Computer Interface ◽  
Multivariate Empirical Mode Decomposition ◽  
Subject Specific

The performance of a brain–computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific “multivariate empirical-mode decomposition” preprocessing technique by taking a fixed band of 8–30[Formula: see text]Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

scholarly journals Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ibrahim Hossain ◽  
Abbas Khosravi ◽  
Imali Hettiarachchi ◽  
Saeid Nahavandi
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Training Session ◽  
Training Data ◽  
Superior Performance ◽  
Computer Interface ◽  
Imagery Task ◽  
Learning Framework ◽  
Optimal Ensemble

A widely discussed paradigm for brain-computer interface (BCI) is the motor imagery task using noninvasive electroencephalography (EEG) modality. It often requires long training session for collecting a large amount of EEG data which makes user exhausted. One of the approaches to shorten this session is utilizing the instances from past users to train the learner for the novel user. In this work, direct transferring from past users is investigated and applied to multiclass motor imagery BCI. Then, active learning (AL) driven informative instance transfer learning has been attempted for multiclass BCI. Informative instance transfer shows better performance than direct instance transfer which reaches the benchmark using a reduced amount of training data (49% less) in cases of 6 out of 9 subjects. However, none of these methods has superior performance for all subjects in general. To get a generic transfer learning framework for BCI, an optimal ensemble of informative and direct transfer methods is designed and applied. The optimized ensemble outperforms both direct and informative transfer method for all subjects except one in BCI competition IV multiclass motor imagery dataset. It achieves the benchmark performance for 8 out of 9 subjects using average 75% less training data. Thus, the requirement of large training data for the new user is reduced to a significant amount.

scholarly journals Selective Cross-Subject Transfer Learning Based on Riemannian Tangent Space for Motor Imagery Brain-Computer Interface

2021 ◽  
Vol 15 ◽  
Author(s):  
Yilu Xu ◽  
Xin Huang ◽  
Quan Lan
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Specific Model ◽  
Covariance Matrices ◽  
Computer Interface ◽  
High Temporal Resolution ◽  
Eeg Signals ◽  
Target Subject

A motor imagery (MI) brain-computer interface (BCI) plays an important role in the neurological rehabilitation training for stroke patients. Electroencephalogram (EEG)-based MI BCI has high temporal resolution, which is convenient for real-time BCI control. Therefore, we focus on EEG-based MI BCI in this paper. The identification of MI EEG signals is always quite challenging. Due to high inter-session/subject variability, each subject should spend long and tedious calibration time in collecting amounts of labeled samples for a subject-specific model. To cope with this problem, we present a supervised selective cross-subject transfer learning (sSCSTL) approach which simultaneously makes use of the labeled samples from target and source subjects based on Riemannian tangent space. Since the covariance matrices representing the multi-channel EEG signals belong to the smooth Riemannian manifold, we perform the Riemannian alignment to make the covariance matrices from different subjects close to each other. Then, all aligned covariance matrices are converted into the Riemannian tangent space features to train a classifier in the Euclidean space. To investigate the role of unlabeled samples, we further propose semi-supervised and unsupervised versions which utilize the total samples and unlabeled samples from target subject, respectively. Sequential forward floating search (SFFS) method is executed for source selection. All our proposed algorithms transfer the labeled samples from most suitable source subjects into the feature space of target subject. Experimental results on two publicly available MI datasets demonstrated that our algorithms outperformed several state-of-the-art algorithms using small number of the labeled samples from target subject, especially for good target subjects.

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